RESEARCH PAPER

Constructing online switching barriers: examining the effectsof switching costs and alternative attractiveness on e-store loyaltyin online pure-play retailers

Ezlika Ghazali1 & Bang Nguyen2& Dilip S. Mutum3

& Amrul Asraf Mohd-Any1

Received: 15 June 2015 /Accepted: 26 February 2016 /Published online: 11 March 2016# Institute of Applied Informatics at University of Leipzig 2016

Abstract Developing switching barriers to retain customershas become a critical marketing strategy for online retailers.However, research on the role of switching barriers in e-retailing is still limited. Recent trends show that when compet-itors are just one click away, it is questionable if customer loy-alty can be achieved at all in online environments. This leads tothe research question on whether switching barriers have anyimpact on e-loyalty in pure-play retailers. The paper examinesthe influence of switching barriers on customer retention (i.e., e-store loyalty) and further investigates the moderating effects ofswitching costs and alternative attractiveness. Data were gath-ered via a survey of 590 shoppers of online pure-play retailers inthe UK. Findings show that customer satisfaction and the twodimensions of switching barriers (perceived switching costs andperceived attractiveness of alternatives) significantly influencecustomer loyalty. Contrary to findings in earlier studies, it wasfound that switching costs did not moderate the relationshipsbetween satisfaction and loyalty nor between perceived attrac-tiveness of alternatives and loyalty. The paper makes imperativerecommendations to develop switching barriers and to fosterloyalty along with suggestions for future research.

Keywords Switching barriers . Switching costs . Alternativeattractiveness .Customer satisfaction .E-store loyalty .Onlinepure-play retailers

JEL Classification M3Marketing . Advertising

Introduction

The increased competition in the online marketplace and highcosts for getting access to new customers have required e-retailers to continually emphasize on customer retention andloyalty strategies (Otim and Grover 2010; Polo et al. 2011).Customer retention and loyalty is especially acute in the on-line environment since the “competing offer is just a fewclicks away” (Shankar et al. 2003, p. 154). For example,Harris and Goode (2004, p. 139) note that “generating loyalcustomers online is both more difficult and important than inoffline retailing.” Balabanis et al. (2006) show that some sat-isfied customers might not consider themselves loyal to the e-stores, indicating that acquiring and retaining customers on-line is not particularly straightforward. However, customerretention has become “an economic necessity” (Balabaniset al. 2006, p. 214), as the cost of acquiring new customerscan be up to five times as costly as maintaining existing ones(Bauer et al. 2002). Consequently, for retailers operating in theonline environment, developing switching barriers to induceloyalty has become a critical marketing strategy (Alt andÖsterle 2013).

Despite the critical role of switching barriers in mar-keting, research in this area have focused predominantlyon the offline context (e.g., Jones et al. 2002; Burnhamet al. 2003). More research is needed to understand theeffectiveness of switching barriers in online retailing(e.g. Goode and Harris 2007; Mutum et al. 2014),

Responsible Editor: Christopher P. Holland

* Bang Nguyenbang.london@gmail.com

1 Department of Marketing, Faculty of Business & Accountancy,University of Malaya, 50603 Kuala Lumpur, Malaysia

2 School of Business, East China University of Science andTechnology, 130 Meilong Road, Xuhui District 200237, Shanghai,People’s Republic of China

3 Nottingham University Business School Malaysia, The University ofNottingham Malaysia Campus, Jalan Broga,Semenyih 43500, Selangor, Malaysia

Electron Markets (2016) 26:157–171DOI 10.1007/s12525-016-0218-1

especially as the competition intensifies and consumershave numerous alternatives to which they can switchvery easi ly (e.g. , Bakos 1997; Friedman 1999;Holloway 2003). Hence, it is particularly important to under-stand the role of attractiveness of alternatives in developingswitching barriers. In addition, while some studies haveemployed switching costs as their main construct, there hasbeen very little effort to develop robust measures of onlineswitching costs. The multifaceted typologies and dimensionsof switching costs denote the complex nature of the construct.Yet, empirical research in this area has treated switching costsas unidimensional (e.g. Fuentes-Blasco et al. 2010; Goode andHarris 2007; Kim and Son 2009), measuring such costs as aglobal construct. This approach ignores the conceptual rich-ness of the switching costs construct, which is too complex tobe operationalized as unidimensional (Burnham et al. 2003).Aydin et al. (2005) specifically highlight the need for furtherresearch to investigate the sub-dimensions of switching costs.

In addition, Carlson and O’Cass (2011) highlight the im-portance of distinguishing between online pure-play retailers(online retailers with no physical retail presence) and bricks-and-clicks companies (retailers with both online and offlinepresence). Holloway (2003) showed that with bricks-and-clicks retailers, stronger consumer relationships can be devel-oped compared to online only retailers. Thus, failure to differ-entiate between the two types of online retailers (pure-play vs.bricks-and-clicks) may further distort the results or lead toover-estimation of relationships. A review of extant researchin switching behavior reveals that no studies have looked atcustomer retention of online pure-play retailers.

To fill the above gaps, this research represents the first at-tempt at studying switching barriers explicitly with respect toonline pure-play retailers. The aims of this study are threefold.(1) First, the study develops an online customer loyalty frame-work of customer retention/switching by explicating switchingbarriers. By capturing the key factors likely to trigger e-loyalty,the study is unique in studying the role of alternative attractive-ness and switching costs conjointly in the context of loyaltyand satisfaction towards an online retailer (Chen and Hitt2002). (2) Second, the study incorporates and evaluates themoderating effects of switching costs in order to provide fur-ther insights into the nature of e-loyalty, previously suggestedas an area for future research (Mutum et al. 2014). (3) Finally,the study advances the literature on online marketing with thevalidation of a multi-dimensional measurement scale ofswitching barriers (Pappas et al. 2014). Thus, the study fillsimportant gaps in the online marketing knowledge that linksswitching costs, alternative attractiveness, and e-store loyalty.

With an increase in popularity of online pure-play retailers,this study is also important from a managerial perspective. Arecent report (Retailpro 2015) revealed that 44 % of Britishshoppers do more than half of their online shopping on pure-play retailers. Thus, for online retailers, implications exist in

constructing switching barriers that increase customer satis-faction and loyalty. By articulating customer-focused strate-gies that foster loyalty, the study is expected to help managerswith the validation of a multi-dimensional measurement scaleof switching barriers, providing a useful tool to operationalizeswitching barriers. The rest of the paper is organized as fol-lows: first, the study reviews the literature on online switchingcosts, identifying the existing gaps and developing thehypotheses. Subsequently, it discusses in detail the researchmethodology employed in this study. This is followed by theresults of the analysis. Finally, the study findings arepresented, discussed, and concluded.

Literature review

In the section that follows, the paper presents the key literaturepertaining to online switching barriers and customer loyalty.This provides an overview of extant literature and reveals thegaps which the study aims to fill.

Building customer retention and loyalty

Ranaweera and Prabhu (2003) noted two principal strategiesto building customer retention. The first is to improve custom-er satisfaction, so that the customer ‘wants’ to stay with thefirm. The second is to increase the perception of ‘switchingbarriers’, which impede customer switching. These strategiescould also be applied to the online retail setting to inducecustomer loyalty. Oliver (1999) describes loyalty as the over-all attachment and deep commitment to product, brand, orga-nization, or retailer. Customer loyalty is crucial for a firm’ssuccess because loyal customers enhance its profitability(Reichheld 1996), market share (Chaudhuri and Holbrook2001), and shareholder value (Sindell 2000). The importanceof customer loyalty cannot be emphasized enough in the con-text of online marketplaces, especially for pure-players, whereit is generally held that customers are able to terminate a rela-tionship with a mere ‘click of the mouse’ (Anderson andSrinivasan 2003; Reichheld and Schefter 2000). Therefore,without core loyal customers, pure-players may find it diffi-cult to remain afloat because developing loyalty is not as clearcut as some studies have suggested (e.g., Coelho and Henseler2012; Emanuelsson and Uhlén 2007). For example, despitebeing assumed as a prerequisite to customer loyalty, customersatisfaction does not automatically predict loyalty. Previousstudies have established imperfect correlations between thetwo constructs, and the strengths of their relationships remainhighly questionable (e.g., Balabanis et al. 2006; Dagger andDavid 2012; Emanuelsson and Uhlén 2007). Hence, wheninvestigating the loyalty inducing influences (Coelho andHenseler 2012), satisfaction along with other factors that drivecustomer retention should be considered, including switching

158 E. Ghazali et al.

costs, competitors’ offerings (Burnham et al. 2003; Jones et al.2002), and alternative firms (Roberts 1989).

Understanding switching barriers

When the perception of switching barriers is high and theoptions to exit a relationship are limited, the tendency forloyalty increases (Hirschman 1970). A useful definition ofswitching barriers is provided by Jones et al. (2000, p. 261),who conceptualized it as “…any factor, which makes it moredifficult or costly for consumers to change providers.”However, there is some confusion between the terms‘switching barriers and ‘switching costs’ (Balabanis et al.2006; Colgate et al. 2007), with many authors using theminterchangeably (e.g. Bansal and Taylor 1999; Mathwick2002). Although Goode and Harris (2007) noted “subtle dif-ferences between switching barriers and costs” (p. 517), theyfail to describe any clear differences.

Ranaweera and Prabhu (2003) assert that the switchingbarriers construct encompasses ‘self-efficacy’ as well as‘facilitating condition’ issues. Other authors, namelyBurnham et al. (2003) and Jones et al. (2000, 2002), haveconceptualized switching barriers (offline service context)as a multidimensional construct encompassing several cat-egories and dimensions. According to Jones et al. (2000,2002), switching barriers is made up of three main dimen-sions, namely, perceived switching cost, attractiveness ofavailable alternatives, and interpersonal relationships,while switching barriers as conceptualized by Burnhamet al. (2003) does not include attractiveness of alterna-tives. In this research, we adapt and extend the frameworkconceptualized by Jones et al. (2000), consideringswitching barriers as a multidimensional construct.However, since the focus of this research is on the cus-tomers of pure play online retailers, the interpersonal re-lationship construct does not apply because compared to aphysical market environment, there is a considerable lackof face-to-face interaction (Szymanski and Henard 2001)between customers and retailer employees in the onlinemarket, which precipitates the importance of the interper-sonal relationship component. As such only perceivedswitching costs and attractiveness of alternatives are ex-amined in this research. These key concepts and theirantecedents are explained in the subsequent sections.

Barrier 1: Perceived switching costs

In the B2C context, Patterson and Smith (2003) defineswitching costs as “the perception of the magnitude of theadditional costs required to terminate a relationship and securean alternative one” (p. 108). Some researchers havequestioned the credibility of a single global measure to repre-sent perceived switching costs, although most studies have

conceptualized it as unidimensional. According to Fornell(1992), perceived switching costs are complex and as suchrequire a higher level of abstraction. Fornell also suggests thatdue to its complex nature, perceived switching costs are diffi-cult to measure. Although researchers have questioned thecredibility of its single global measure, there have been onlyfew attempts to empirically measure it as a multifaceted con-struct (e.g. Burnham et al. 2003; Jones et al. 2002). Most ofthese studies are done in the offline context. Due to theseinconsistencies, there is a need to unify the current theoreticaldiscussion and to develop a set of switching costs pertinent toother contexts, such as an online retail setting.

Based on a comprehensive literature review (Balabaniset al. 2006; Burnham et al. 2003; Colgate et al. 2007;Fornell 1992; Jones et al. 2002; Thatcher and George 2004;Mutum et al. 2014), we identified five dimensions of per-ceived switching costs (see Table 1). As perceived switchingcosts are highly salient in the online environment where cus-tomers are often co-producers of the services they receive,three categories of online switching costs are identified.They are: the procedural costs components (learning costs,search and evaluation costs, and uncertainty costs), the eco-nomic or monetary costs component (artificial costs), and therelationship-based or psychological costs component (brandrelationship loss costs).

Barrier 2: Attractiveness of alternatives

Another construct underlying customer online switchingbarriers is that of perceived alternative attractiveness. Thisrefers to customers’ perceptions of the extent to whichviable competing alternatives are available in the market-place (Jones et al. 2000). Individuals’ commitment to arelationship should increase when they are satisfied withthe relationship and/or when there are no good alterna-tives available. Further, not only does a large perceiveddifference among alternatives lead to customer retention,but the lack of perceived differences also influences cus-tomers to stay in existing relationships. According toPatterson and Smith (2003), when a customer perceivesthat alternatives are no different from their existing pro-vider or does not perceive them as ‘any more attractive’than their existing relationship, they tend to remain loyalto their current provider. In this situation, the customer’sperception is that switching is not worthwhile (Colgateand Lang 2001) because the net benefit from alternativerelationships is not superior to the current relationship(Hennig-Thurau et al. 2000). Overall, the offline market-ing literature suggests the presence of at least three factorsaffecting customers’ perceptions of the attractiveness ofalternatives: existence of alternatives, heterogeneity (se-verity of difference) among alternatives, and highswitching costs between alternatives. Based on these

Constructing online switching barriers 159

factors, we label the three dimensions of alternativeattractiveness (Table 2) for the online pure-play marketas retailer indifference, alternative awareness, and alter-native preference.

On the whole, our review of the literature, lookinginto factors influencing customer retention in the onlineenvironment, revealed that research into switching bar-riers is, unquestionably, lacking (Mutum et al. 2014).Therefore, extant theories and normative insights vis-à-vis customer retention/customer switching in the contextof the online milieu need to be re-examined. In addition,previous research on switching behaviors of customers,have predominantly focused on the offline purchasingcontexts. The limited number of online studies onswitching behaviors of customers has used hybrid sam-ples, that is, they have not differentiated between cus-tomers of brick-and-click and pure-player online re-tailers. This study addresses these above gaps by scruti-nizing online customer loyalty/switching behaviors onlyin the context of pure-player entities.

Hypothesis development

Building on the literature review, this section develops thestudy’s framework and corresponding hypotheses.Relationships between the key constructs are theorized andhypotheses are put forward for subsequent testing, here inthe context of online pure-play retailers.

Customer satisfaction and loyalty

Customer satisfaction is one of the most critical con-structs and a core concept in marketing (Garbarino andJohnson 1999; Holloway 2003; Mittal and Kamakura2001). This study focuses on the overall satisfaction, thatis, the cumulative judgment overtime of customers withregards to a retailer’s performance (Anderson et al. 1994;Burnham 1998). A few authors have argued that satisfac-tion is not a prerequisite to loyalty and/or customer re-tention. In other words, increased satisfaction may notnecessarily lead to an increase in loyalty to firms, andevidence shows that dissatisfied customers often remainwith a retailer (Bolton and Drew 1991; Burnham et al.2003) and that satisfied customers still buy elsewhere(Keaveney 1995) or do not necessarily buy more(Seiders et al. 2005). Thus, the role of satisfaction ininfluencing loyalty is more complicated than initiallythought (Dagger and David 2012; Mittal and Kamakura2001; Oliver 1999).

However, there is substantial empirical evidence in the liter-ature linking global cumulative satisfaction to loyalty (Oliver1997; Szymanski and Henard 2001). In essence, consumers are

likely to develop positive intention towards behavior (i.e., re-peat purchase or online transaction) (Albert et al. 2014), if theyhave a positive attitude (i.e., feeling of satisfaction based onpast performance) towards the behavior. Thus, in this paper,customer satisfaction is hypothesized to be associated with loy-alty, which is conceptualized as the ‘mindful’mode of custom-er repeat purchase. Hence, we postulate that:

H1: Satisfaction positively affects loyalty, in online pure-play retailers.

Attractiveness of alternatives and loyalty

Customer consideration of alternatives is a key element inmaking choices about whether to stay or defect (Pattersonand Smith 2003; Rust and Kannan 2003). Past studies havefound that when switching costs are perceived as high, asexperienced by customers, such high switching cost percep-tions will have a negative influence on the attractiveness ofalternatives. In other words, customers consider that switchingto other companies is less desirable due to higher switchingcosts and consequently, the customer eventually loses interestin the company’s competitors (Kim and Son 2009). On thewhole, there is a tendency for switching cost perceptions toreduce (a) the level of customer consideration of other alter-natives (Heide and Weiss 1995), (b) the customer’s effort insearching for alternatives (Weiss and Heide 1993) as well as(c) their propensity to search for alternatives (Zauberman2003).

As the perceived attractiveness of alternatives increases,customers are more likely to be involved in solving problemsand less likely to remain loyal (Hirschman 1970; Ping 1993;Rusbult et al. 1982) and the probability of switching increases(Bendapudi and Berry 1997; Jones et al. 2000; Sharma andPatterson 2000). Rusbult et al. (1982) observed that the per-ception of high quality alternatives positively influences exitand negatively influences loyalty. Similarly, Jones et al.(2002) and Yim et al. (2007) showed that attractiveness ofalternatives had negative effects on commitment and repur-chase intention. Thus, we hypothesize that:

H2: Attractiveness of alternatives inversely affects loyal-ty, in online pure-play retailers.

Switching costs and loyalty

According to Jones et al. (2002, p. 441), switching costs arebarriers that “hold customers in service relationships.”Switching costs in this study refers to ‘perceived’ switchingcosts (Morgan and Hunt 1994) as it is not any objective cost

160 E. Ghazali et al.

that will be measured, but rather the switching costs as per-ceived by customers (Burnham et al. 2003).

Fornell (1992) suggests that while satisfaction makes itharder for competitors to take away a firm’s customers,switching costs make it costly for customers to defect to com-petitors. Indeed, switching costs can be more critical an ante-cedent to customer retention than satisfaction because cus-tomers tend to attribute greater weight to them when makingdecisions (Dick and Basu 1994). Many scholars agree that onepotential but crucial antecedent to loyalty is switching cost(e.g., de Ruyter et al. 1998; Rust and Kannan 2003). It hasalso been argued that switching costs positively influence loy-alty and retention (Fornell 1992). For example, Patterson andSmith (2003) and Bell et al. (2005) found significant directeffects of switching costs on customers’ propensity to remainwith a firm. This is also evident in the online B2C relation-ships (e.g., Anderson and Srinivasan 2003; Chen and Hitt2002). The process of changing to alternatives will involve

extra investment in searching, evaluating, and filtering infor-mation. Therefore, we postulate that:

H3: Perceived switching costs positively affects loyalty,in online pure-play retailers.

Moderating effects of switching costs

The above discussion sets the foundation for the study’s finalhypotheses, which posit that the effects of switching costs arenot only evident in its direct effect on e-loyalty, but is alsoshown indirectly, as a moderation variable, through its morecomplex influence on satisfaction and alternative attraction, inthe online pure-play market. Most offline retailing studieshave regarded switching costs as a moderator in satisfactionand loyalty relationships (Yang and Peterson 2004). For in-stance, Burnham et al. (2003) found that switching costs im-pose a moderating effect on repurchase intention through

Table 2 Dimensions of alternative attractiveness

Dimension Description Source

Retailer Indifference The overall perception that because most otherretailers are similar (i.e., retailer indifference),it may not be worthwhile to search for alternatives.

Similar to Balabanis et al.’s (2006)‘parity barriers’

Alternative Awareness The customers’ high awareness of alternatives(i.e., competitors) in the market, possibly due tothe customers’ experience in online shopping.

Comparable to Balabanis et al.’s (2006)‘unawareness barriers’

Alternative Preference The customers’ preference for alternatives’(competitors’) service and offerings to their currentretailer’s.

Concurs with Li et al. (2006) andRusbult et al.’s (1998) ‘quality ofalternative’ dimension

Table 1 Components of perceived switching costs

Dimension Description Source

Learning costs Expenditure of time and effort to learn, understand or use thenew service effectively. This includes familiarizing withconducting transactions on an unfamiliar website.

Burnham (1998, p. 107)

Jones et al. (2002)

Artificial costs Actions initiated by a firm to retain customers and to make itmore expensive for them to switch suppliers. For example,frequent flyer program.

Klemperer (1987)

To (1996, p. 31)

Uncertainty costs Customer’s perception of future costs or losses associatedwith possible negative consequences incurred by switchingto an unfamiliar or untested retailer. This is closely linked tothe perception of risk such as performance risk, financialrisk, convenience risk and privacy and security risks.

Colgate and Lang (2001)

Mitchell (1999)

Burnham et al. (2003)

Forsythe and Shi (2003)

Search and evaluation costs It incurs when searching for a suitable alternative retailer toswitch to. Two types of search costs as potential reasonsfor customers remaining with a retailer (lock-in); ‘physicalsearch cost’ and ‘cognitive search cost’.

Chen and Hitt (2002)

Bakos (1997)

Johnson et al. (2003)

Brand relationship loss costs The feeling of loss in leaving a brand. Strong brand imageand positive brand attitude reinforce the relationshipbetween customers and retailers, making the switchingprocess more costly.

Burnham et al. (2003)

Polo and Sesé (2009)

Constructing online switching barriers 161

satisfaction in two service industries (viz., credit card andphone call service). Similar results were observed in mobilephone service (Lee et al. 2001). Hauser et al. (1994) reportedthat a strong level of perceived switching costs reduces thesensitivity of a customer to perceived satisfaction. Likewise,Anderson and Sullivan (1993) discovered a negative relation-ship between switching costs and customer satisfaction sensi-tivity in the banking industry.

However, opinion with regards to the moderating role ofswitching costs in the e-retailing environment is quite mixed.Some scholars argue that this role may not always be signif-icant and will depend on other variables such as the types ofbusiness, customers or products (Nielson 1996). For example,Balabanis et al. (2006) found that switching costs will onlymoderate the e-satisfaction and loyalty links, when e-satisfaction is higher than average. However, Holloway(2003) while looking at online service failure recovery, foundno moderating effect of switching costs on the relationshipbetween satisfaction and repurchase intention. Similarly,Chen and Hitt (2002) did not find any moderation as well.Due to the mixed findings in prior research, the moderatingrole of switching costs warrants further investigation.Therefore:

H4: Perceived switching costs will moderate the relation-ship between satisfaction and loyalty.

Attraction towards alternatives in the market will stronglyand negatively influence the development of loyal customers.However, the prevalence of switching costs can serve as

a buffer against the negative impact of high attractive-ness of alternatives on loyalty. For example, a customermay feel that there is price unfairness if a competitoroffers a lower price compared to their current e-retailer.The logical action of the customer is to end the currentrelationship and establish a new one with the competitor.This action, however, is not without any cost (Xia et al.2004). If the customer decides to leave the relationship,they may incur switching costs that include time, effort,and money. Thus, the costs of action will most likelymoderate the relationship between attractiveness of alter-natives and loyalty. The empirical evidence with regardsto this effect has been negligible (Holloway 2003) in theliterature. Hence, we postulate that:

H5: Perceived switching costs will moderate the relation-ship between attractiveness of alternatives and loyalty.

Figure 1 shows our conceptual framework.

Research method

To achieve the aims of the research, a quantitative re-search methodology was conducted. This section presentsthe research that was implemented to test the framework,including the data collection and succeeding rigorous pro-cess of data analysis.

Overall Satisfaction

E-Loyalty Alternative Attractiveness

BR LC AC UC SEC

Perceived Switching Costs

H3+

H2-

H5+

H4+

H1+

Note: RI: Retailer Indifference; AP: Alternative Preference; AA: Alternative Awareness are derived and labelled after running CFA LC: Learning Costs; AC: Artificial Costs; UC: Uncertainty Costs; SE: Search and Evaluation Costs; BR: Brand Relationship

Direct effect

Moderating effect

RI

AP

AA

Fig. 1 Conceptual framework ofswitching barriers. RI: RetailerIndifference; AP: AlternativePreference; AA AlternativeAwareness are derived andlabelled after running CFA. LCLearning Costs, AC ArtificialCosts, UC Uncertainty Costs, SESearch and Evaluation Costs, BRBrand Relationship

162 E. Ghazali et al.

Sample

We conducted a questionnaire-based survey to examine theimpact of online customer switching barriers on loyalty of arange of pure ‘dot com’ retailers popular with consumers inthe UK which offer various types of products and services.The respondents were required to evaluate one e-retailer fromwhich they most frequently purchased. This method ofsoliciting respondents to report their experience with pro-viders has been widely accepted in prior research (e.g.Balabanis et al. 2006; Li et al. 2006). As a guideline, thesample must be composed of individuals in the UK who areInternet shoppers of pure online retailers. According to theONS (2014), two in every five adults (40 %) aged 65 and overbought goods or services online in 2014. This is more thandouble the 2008 estimate (16 %). A mailing list comprising arandom sample of 4000 consumers was purchased for singleuse from Experian UK’s pre-existing database of online shop-pers. The sample was restricted to online shoppers over theage of 16, who are categorized as adults by the Office forNational Statistics UK, and who had transacted at least oncein the last 6 months.

Data collection

Data were collected via a self-administered questionnaireusing a hybrid survey approach (i.e., web with mail design)(Dillman et al. 2009) in order to reduce non-response error (deLeeuw et al. 2008). A packet containing a paper questionnaire,a personalized cover letter, and postage paid returned enve-lope were sent out to all the potential respondents. They weregiven the choice to complete via either paper or online ques-tionnaire hosted on Surveymonkey.com. Out of the 4000questionnaire packets mailed, 163 were returned as ‘undeliv-ered’, leaving 3837 potential respondents. After a month, atotal of 799 responses were received of which 578 camethrough the post, while the rest (221) were collected online.Since our study focuses on pure-play firms, 209 responseswere removed - either because they were unusable or becausethey chose websites of companies with strong offline presenceas well. A total of 590 responses were used for the final anal-ysis, which corresponds to approximately 15.4 % responserate. Table 3 presents the profiles of the respondents. We com-pared estimates of demographic characteristics to the generalpopulation1 to assess the quality of the sample in terms of bothnon-coverage and non-response and can conclude that oursample is representative of adult Internet shoppers in the UK.

Table 4 presents the online retailers selected by the respon-dents in descending order of frequency. The findings showthat the three most dominant pure-players for consumers in

the UK are Amazon UK, Play.com, and Amazon.com (US).Amazon UK is quite distinct from the US site with respect toseveral features and some products are available exclusivelyon each of the sites.

The respondents are experienced shoppers on their favoritee-retailer in general. More than half of the respondents havebeen purchasing from the e-retailer for more than 3 years.Most respondents also indicated that they had visited thewebsite at least once every 2 months, with one-third of therespondents doing so at least twice a month. With respect tothe frequency of purchase from their retailer’s website, morethan half of the respondents purchased at least once every3months with a number of purchases beingmade at least threeto five times a year. In terms of money spent on their retailer’swebsite, 66 % of the respondents spent between £10 and £40per transaction. The products purchased most frequently werebooks and CDs/DVDs. Other popular products includedclothing, electrical items, and computers.

Measures

Respondents were asked to indicate their level of agreementon 7-point Likert-type scales derived from previous studies,anchored by 1 = strongly disagree and 7 = strongly agree. Tooperationalize overall satisfaction, we adapted three modifieditems from Voss et al. (1998), Seiders et al. (2005), andBourdeau (2005). We adapted the loyalty scale using fiveitems from Bourdeau (2005). The five second-order dimen-sions of switching cost were measured as follows: 6-itemlearning cost, 7-item artificial cost, 6-item uncertainty cost,5-item search and evaluation cost, and 3-item brand relation-ship loss (Bourdeau 2005; Burnham et al. 2003; Holloway2003; Jones et al. 2007; Korgaonkar and Wolin 1999;Mathwick 2002). Attractiveness of alternatives was measuredusing nine items (Burnham 1998; Holloway 2003; Li et al.2006; Ping 1993). Table 5 provides a list of the measures andtheir psychometric properties after purification. Our question-naire was pre-tested by two academics who are experts in thearea of e-service and consumer behavior followed by another15 individuals who are familiar with online shopping. Thehypotheses are tested following the assessment of psychomet-ric properties of the measurement scale. Structural equationmodeling is used for this purpose using AMOS 18 softwarepackage.

Reliability and validity of measures

Confirmatory factor analysis was used to assess the validityand reliability of our multi-item scales (Gerbing and Anderson1988). As shown in Table 5, all the average variance extractedand composite reliabilities exceed the standard thresholds inthe literature (Bagozzi and Yi 1988). Though the loadings ofUC1 (0.58) and BR2 (0.51) are lower than the recommended

1 Population reports were derived from Nielson-Online (2009), Mintel-Oxygen (2007) and British Population Survey (January 2008).

Constructing online switching barriers 163

value of 0.6 (Bagozzi and Yi 1988; Chin 1998), we retainedthem to support content validity (Hair et al. 2010, p. 715).Consistent with the literature, our analysis confirmed that theconstruct Alternative Attractiveness must be viewed at ahigher level of abstraction with three second-order sub-dimen-sions. However, from the nine proposed items to measure thisconstruct, three ‘problematic’ itemswere removed, leaving sixitems reflecting the sub-dimensions. Similar to Balabanis

et al.’s (2006) ‘parity barriers’ and ‘unawareness barriers’,we named the first two dimensions ‘Retailer Indifference’and ‘Alternative Awareness’, respectively. RetailerIndifference refers to the overall perception that because mostother retailers are similar, it may not be worthwhile searchingfor alternatives.Alternative Awareness refers to the customers’high awareness of competitors in the market, possibly due tothe customers’ experience in online shopping. The third factor

Table 3 Comparativedemographic profile ofrespondents (N= 590)

Demographic profile Category Frequency Percent

Personal income per annum: Less than £15,000 90 15.3

£15,000–£19,999 52 8.8

£20,000–£24,999 53 9

£25,000–£29,999 53 9

£30,000–£49,999 146 24.7

£50,000–£75,000 172 29.2

Total 566 96

Not Disclosed/Refused 4

Missing Value 20

Age: 16–24 52 8.8

25–34 174 29.5

35–44 175 29.6

45–54 99 16.8

55–64 60 10.2

65 and over 30 5.1

Total 590 100

Sex: Male 317 53.7

Female 270 45.8

Total 587 99.5

Missing Value 3

Race: White 436 73.9

Black 21 3.6

Mixed 11 1.9

Asian 102 17.3

Middle Eastern 8 1.4

Other 3 0.5

Total 581 98.5

Not Disclosed/Refused 3

Missing Value 6

Table 4 Respondents’ selectionof pure-play online retailers Pure-play online retailers Product description Frequency (%)

Amazon UK Books, CDs, consumer electronics etc. 385 (65)

Play.com Computers, consumer electronics, apparel etc. 42 (7.1)

Amazon US Books, CDs, consumer electronics etc. 40 (6.8)

ASOS Apparel 15 (2.5)

Ebuyer Computers, consumer electronics 6 (0.8)

Ryanair Cheap flights 5 (0.8)

Expedia UK Holiday/travel products 3 (0.5)

164 E. Ghazali et al.

Table 5 Measures and CFAresults Scale/Item CR AVE Factor

loadingt-Values

Mean SE

E-loyalty .75 .50 3.63 .052

1. When I have a need for this type of product, I willuse only this online retailer.

.82 13.08

2. I would not even consider another online retailer forthis product.

.68 10.99

3. I am unlikely to switch to another online retailer inthe near future.

.60 9.72

Overall satisfaction .84 .63 5.74 .035

1. I am pleased with the overall service. .79 14.84

2. Overall, I am completely satisfied with my shoppingexperience.

.81 15.47

3. When I think about my shopping experience here, Iam generally pleased.

.79 14.82

Switching costs (second-order)

Learning Cost (LC) .85 .53 3.00 .043

1. Getting used to new website after I switch would bevery easy. (r)

.66 11.99

3. Switching my shopping activities to another onlineretailer would require too much learning.

.84 16.80

4. I feel that the competitors’ websites are difficult touse.

.66 11.89

5. I am reluctant to change online retailer because I amfamiliar with ‘how the system works’ on thiswebsite.

.73 13.83

6. It takes time/effort to understand how to use otheronline retailers’ websites.

.71 13.18

Artificial Cost (AC) .90 .69 2.68 .055

1. I receive special rewards and discounts from doingbusiness with this online retailer.

.84 17.31

2. I will lose the benefits of being a long-term customerif I leave my online retailer.

.85 17.60

3. Staying loyal gives me discounts and special deals. .89 18.74

4. Staying loyal saves me money. .73 14.02

Uncertainty Cost (UC) .87 .62 4.25 .050

1. I am concerned about the security of my personalinformation when registering on a new website.

.58 10.21

2. I worry that switching my shopping activities toanother online retailer would result in someunexpected problems.

.85 16.63

3. If i were to change online retailer, i fear that theservice I would receive might worsened.

.76 14.44

4. Switching to another online retailer would be risky,since I wouldn’t know the quality of its products/services.

.76 14.27

Search and Evaluation cost (SE) .81 .52 4.16 .056

1. I don’t like spending time searching for a new onlineretailer.

.74 13.43

2. I cannot afford the time/effort to evaluate alternativeonline retailers fully.

.78 14.44

3. Comparing the competitors in order to work outwhich best suits my needs is a time-consuming task.

.65 11.51

4. I don’t think that the process of evaluating a newonline retailer prior to switching would be a hassle.(r)

.69 12.40

Brand Relationship Loss (BR) .73 .49 3.60 .055

1. The brand of this retailer plays a major role in mydecision to stay.

.76 13.28

2. I do not care about the brand/company name of theonline retailer that I use to buy this product. (r)

.51 8.23

Constructing online switching barriers 165

concurs with Li et al. (2006) and Rusbult et al.’s (1998) ‘qual-ity of alternative’ dimension and is labeled AlternativePreference, reflecting the customers’ preference for a compet-itor’s service and offerings to their current retailer’s.

With regard to the two second-order switching barriersconstructs (switching costs and alternative attractiveness),we conducted additional tests to assess convergent validityby linking the first-order dimensions to their second-orderglobal construct and the path coefficient estimates must besignificant (Benson and Bandalos 1992). As a result, conver-gent validity was established for switching costs and attrac-tiveness of alternatives. To assess discriminant validity be-tween all constructs, we examined the Chi-square (χ2) differ-ence between the standard model and the ‘non-discriminant’model at unity (Anderson and Gerbing 1988; Bagozzi et al.1991) and found that the constructs and sub-constructs in ourmodel are distinct from each other; hence discriminant valid-ity was confirmed as well.

Results of the hypotheses tests

Results of structural equation modeling (Table 6) show theoverall good fit measures are all within the thresholds indicat-i n g a c c e p t a b l e f i t ( r e = 1743 ; ( = 1d f ) = 1 . 9 7 2 ;NNF I = 0 . 99 > . 95 = good f i t ; CF I = 0 . 929 > . 90 ;RMSEA=0.040< .05=good fit; SRMR=0.057< .08). TheNFI, GFI, and AGFI measures are not reported here becausethey are no longer recommended (Hu and Bentler, 1999;Kenny 2014). H1 is supported (1=0.10; p<0.05), which in-dicates that overall satisfaction is positively related to loyalty.As reflected by the strong and highly significant negative re-lationship between alternative attractiveness and loyalty,

alternative attractiveness acts as an important driver in reduc-ing loyalty. H2 is therefore supported (β=−0.58; p<0.01).Similarly, as perceived switching costs is posited to have aneffect on loyalty, H3 is also supported (β=0.18; p<0.01).

H4 and H5 argue that switching costs moderate the rela-tionship between overall satisfaction and loyalty, and alterna-tive attractiveness and loyalty, respectively. To test this, weapplied the orthogonalization or residual centering approach(Little et al. 2006), whereby the 4-step approach in creatingorthogonalized latent interaction variables was strictly follow-ed. This procedure, to a large extent, eliminatesmulticollinearity issues that have, in the past, plagued manyefforts to model interactions via SEM. Besides, this methodalso uses all possible information of the manifest variables totest the effect, in contrast to previous methods. Compared tothe standard mean-centered approach (e.g., Aiken & West,1991), this approach ensures complete orthogonality betweenthe independent and the interaction variable and hence, leadsto identical inferences and better fitting results (Little et al.2006; Marsh et al. 2007). However, the latent interactionsSAT*PSC and ATA*PSC on loyalty were not significant.Therefore, both H4 and H5 were not supported.

Discussion

Our results are in line with the findings of some earlier studies,which found a positive relationship between satisfaction andloyalty (e.g., Corstjens and Lal 2000; Evanschitzky andWunderlich 2006) and in the online context (e.g., Andersonand Srinivasan 2003; Balabanis et al. 2006; Harris and Goode2004; Methlie and Nysveen 1999). However, our results

Table 5 (continued)Scale/Item CR AVE Factor

loadingt-Values

Mean SE

3. I stay because I like the public image of the retailer. .76 12.95

Alternative Attractiveness (second-order) .86 .76

Retailer Indifference (RI) 4.48 .052

1. I could be buying from a competing website and notnotice much difference.

.92 15.18

2. I would probably be just as happy with the serviceof another online retailer.

.82 13.73

Alternative Preference (AP) 2.99 .045

1. I feel that an alternative online retailer is better thanthis one. (r)

.86 12.03

2. To my mind, another online retailer is closer to myideal. (r)

.75 10.97

Alternative Awareness (AA) 3.89 .050

1. If i had to change online retailer, I know of anotherwhich is just as good.

.68 9.54

2. Compared to this online retailer, there are not manycompetitors with whom I could be satisfied.

.68 9.58

r reversely worded

166 E. Ghazali et al.

indicate that the influence of satisfaction on loyalty in thisstudy is not very substantial, that is, satisfaction explains only10% of the variance in loyalty. This raises the question wheth-er customer defection can be controlled successfully by sim-ply managing customer satisfaction. The results of previousstudies have also found that satisfaction explains very lowvariance in repurchasing behavior (Balabanis et al. 2006;Bolton 1998; Mazursky et al. 1987). According to Reichheld(1996), more than 50% of customers generally defect, despitebeing happy, or even delighted with a company. Even thoughwe found that satisfaction explains only 10 % of variance inloyalty, dissatisfaction may not necessarily result in defection.Therefore, we can conclude that our findings support Chebatet al. (2010), which found that managing satisfaction and/orservice quality are not the only way to foster customer loyalty.However, most companies seem to be bound by this narrowand uncreative belief – the so called ‘satisfaction trap’(Reichheld and Schefter 2000). Another possible explanationis that the linkage between satisfaction and loyalty is non-linear. In other words, loyalty increases only after satisfactionpasses a certain critical threshold (supporting Chebat et al.2010; Dick and Basu 1994; Mittal and Kamakura 2001).This implies that online customers must be highly satisfiedbefore loyalty develops. Thus, retaining customers on the ba-sis of satisfaction metrics alone may not be a very sensiblestrategy.

The findings also reveal that between the two componentsof switching barriers conceptualised in the model, alternativeattractiveness plays a greater role as a driver of customer loy-alty. This is evidenced by the huge negative impact perceptionof alternative attractiveness has in influencing loyalty (stan-dardized coefficient = −0.58 of 58 %). In other words, thelower the perception of good alternatives available in the mar-ket, the more loyal customers will be. It would appear that ane-retailer is not as well protected by new entrants in the onlineenvironment as their offline counterparts, as this result contra-dicts studies in the offline environment. In the offline environ-ment, switching costs play a greater role in determining loy-alty as compared to alternative attractiveness, where compet-itive insulation appears to be more substantial. This has been

found especially among studies on continuous and/or contrac-tual service (e.g., financial, credit card and phone services,etc.).

Our results also provide convincing evidence to concludethat online switching costs have positive and direct effects onloyalty. This is in line with a few studies, which looked atretailing in the offline context (Burnham et al. 2003; Tsaiand Huang 2007; Tsai et al. 2006). Also, it is worth mention-ing here that two previous studies failed to find a significantdirect effect of switching costs on loyalty (viz., Jones et al.2000; Yang and Peterson 2004). Interestingly, our results re-veal that switching costs (18 %) explain loyalty more power-fully as compared to satisfaction (10 %). Previously, Burnhamet al. (2003) found that the influence of switching costs oncustomer intention to purchase to be stronger than satisfaction.The findings show that switching costs are, indeed, a veryimportant factor for loyalty and of course customer retention,contrary to past assertions about the negligibility of switchingcosts in the online context (see for e.g., Holloway 2003;Bakos, 1997).

Our hypothesized moderating effects in the satisfaction-loyalty and attractiveness-loyalty relationships were not sig-nificant. This suggests that switching costs have only directpositive effect on loyalty and not a moderating effect. Thiscontradicts the findings of other past studies in other contexts.For example, Jones et al. (2000), Ranaweera and Prabhu(2003), and Aydin et al. (2005) found negative moderatingeffects of switching costs on service satisfaction andrepurchase intention or loyalty. On the other hand, Lee et al.(2001) found positive moderating effects of switching costson satisfaction and loyalty. It should be noted that these stud-ies involved either ‘high-touch’ offline services (viz., hair sa-lon: Jones et al. 2000) or contractual services contexts (viz.,financial services: Jones et al. 2000; telecommunicationsservices: Aydin et al. 2005; Ranaweera and Prabhu 2003;Lee et al. 2001). The study highlights that customer re-tention of online pure-play retailers is quite unique andwe cannot assume that relationships between satisfaction,switching barriers and loyalty are same as in bricks-andclicks or offline retailers.

Table 6 Hypotheses testingHypothesized parameter Std. β SE t-value Sig. R2 Hyp. Result

SAT → ELOYALTY .10** .062 1.899 .029 H1 Supported

ATA → ELOYALTY −.58*** .196 −5.486 .000 H2 Supported

PSC → ELOYALTY .18*** .109 2.338 .001 H3 Supported

SAT*PSC→ ELOYALTY .04 .174 0.599 .589 H4 Not Supported

ATA*PSC→ ELOYALTY .01 .195 0.119 .905 H5 Not Supported

.53

Model fit: df = 884; t2 = 1743.1; e2 /df= 1.972; NNFI = 0.99; CFI = 0.929; RMSEA=0.040

SAT Satisfaction, PSC Perceived switching costs, ATA Perceived alternative attractiveness

***p< 0.01; **p < 0.05; *p< 0.1

Constructing online switching barriers 167

Conclusion

This paper takes a fresh look on the influence of onlineswitching barriers and customer satisfaction on customer reten-tion, and examines the moderating effects of switching costs inthe context of online pure-play retailers (online retailers whohave no physical retail presence). The study advances currentunderstanding of online customer retention/switching by expli-cating switching barriers from a more nuanced perspective andlinking these to an online customer loyalty framework. Datawere gathered via a survey of 590 shoppers of online pure-playretailers in the UK. Findings show that customer satisfactionand the two dimensions of switching barriers (perceivedswitching costs and perceived attractiveness of alternatives)significantly influence customer loyalty. Contrary to findingsin earlier studies, it was found that switching costs did notmoderate the relationships between satisfaction and loyaltynor between perceived attractiveness of alternatives and loyal-ty. As explained next, the paper has imperative managerialimplications to foster loyalty online.

Managerial implications

This study has several significant implications for marketingmanagers in the online retailing sector. First, managers mustmanage their switching barriers more systematically. As indi-cated by the study, switching barriers ensures that customershave more incentives to remain loyal to the firm. However,managers must remember that managing customer satisfactionis simply not enough to secure loyalty from their customers.They need to be cognizant of the importance of perceivedswitching barriers while preparing marketing plans/initiatives,which seek to maintain customer loyalty (in the online mar-ketplace). In particular, the research findings highlight thestrategic importance of perceptions of barriers to switchingin a more comprehensive manner.

The findings also suggest that there is a need for marketingmanagers to deliberate on the notion of attractiveness of alter-natives, which is closely related to the perception of firm het-erogeneity or differentiation in strategy research. Managersneed to understand that the perceived lack of good alterna-tives, forms formidable barriers to exit and hence is a vitalfactor in customer retention. On the other hand, this is alsorelevant to new start-ups seeking to challenge the moreestablished retailers. Even though the effect of service differ-entiation on customer retention was not evaluated in thisstudy, we suggest offering one-stop shopping; encouraging awider usage of the service through product reviews, creationof wish lists, etc. Online retailers can also offer bundled ser-vices and features such as related item suggestions whenever acustomer buys a product and offers of cheaper or even freepostage for multiple purchases. Securing enough differentialadvantage might help in enhancing loyalty. On the other hand,

as our findings indicate, availability of attractive alternatives isextremely important and is relevant to new start-ups seekingto challenge the more established retailers. Finally, in spite ofthe importance of constructing switching barriers, managersmust be careful not to ‘lock-in’ their customers (Frow et al.2011), as this may be seen as unfair conditions of a sale,leading to perceptions of unfairness, which may damage thebrand (Nguyen et al. 2015). Hence, moderation to the devel-oping of switching barriers as well as consideration of fairnessfrom the consumers’ perspective is preferred to utilizeswitching barriers successfully.

Limitations and future research

While this study has focused on customer perceptions towardsfive unique switching cost dimensions in the context of pure-play e-retailers, future research could focus on the role of eachof the five dimensions vis-à-vis loyalty, specifically, focusingon the importance placed by customers on each of the indi-vidual dimensions. Of particular interest would be to find outwhether the influence of learning costs’ on loyalty would dif-fer from that of artificial costs.

One of the unique contributions of this research is that itfocuses specifically on pure-play online retailers. However,most retailers have both an online and offline presence. Asshown by Shankar et al. (2003), online loyalty does ‘transfer’from the loyalty of traditional (offline) settings. Therefore,future research could compare between pure-players andbricks-and-click companies. Insights from these studies wouldbe useful in shedding light on the role of perceived switchingbarriers as a retention tool. In addition, as our study was basedsolely in the UK where e-commerce is quite mature, furtherstudies could examine the phenomenon in other countries/groups of countries, which have different levels of Internetusage and/or are at different stages of economic development.Further studies could also explore the moderating influence ofvarious demographic and psychographic characteristics suchas age groups, income, family size, and social class.

It should be noted that our study focuses on loyalty and wedifferentiate this from habit or inertia, which refers to behavioror activities performed repetitively, often unintentionally (Jiand Wood, 2007). Future studies should examine habit interms of its measurement and effects on online customer re-tention. It would also be interesting to carry out longitudinalstudies on how customer behavior on pure-play retailers,change over time as these retailers grow. Finally, given theimportance of customer self-participation in value co-creation on the Internet (Mohd-Any et al. 2015), future re-search could investigate the associations between customers’perceived value-in-use and their perceived switching costs.Investigations of the value-in-use concept from a service-dominant logic perspective could deliver significant insightsand could result in an extension of our framework.

168 E. Ghazali et al.

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